Jiang Wei, Yu Weichuan
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Bioinformatics. 2017 Feb 15;33(4):500-507. doi: 10.1093/bioinformatics/btw690.
In genome-wide association studies (GWASs) of common diseases/traits, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is difficult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze datasets from multiple GWASs.
In this paper, we propose a novel summary-statistics-based joint analysis method based on controlling the joint local false discovery rate (Jlfdr). We prove that our method is the most powerful summary-statistics-based joint analysis method when controlling the false discovery rate at a certain level. In particular, the Jlfdr-based method achieves higher power than commonly used meta-analysis methods when analyzing heterogeneous datasets from multiple GWASs. Simulation experiments demonstrate the superior power of our method over meta-analysis methods. Also, our method discovers more associations than meta-analysis methods from empirical datasets of four phenotypes.
The R-package is available at: http://bioinformatics.ust.hk/Jlfdr.html .
Supplementary data are available at Bioinformatics online.
在常见疾病/性状的全基因组关联研究（GWAS）中，我们经常一起分析多个具有相同表型的GWAS，以发现具有更高功效的相关遗传变异。由于难以获取具有详细个体测量数据的数据，基于汇总统计量的荟萃分析方法已成为联合分析来自多个GWAS数据集的常用方法。
在本文中，我们提出了一种基于控制联合局部错误发现率（Jlfdr）的新型基于汇总统计量的联合分析方法。我们证明，在将错误发现率控制在一定水平时，我们的方法是最强大的基于汇总统计量的联合分析方法。特别是，基于Jlfdr的方法在分析来自多个GWAS的异质数据集时，比常用的荟萃分析方法具有更高的功效。模拟实验证明了我们的方法相对于荟萃分析方法的优越功效。此外，我们的方法从四种表型的经验数据集中发现的关联比荟萃分析方法更多。
R包可在以下网址获得：http://bioinformatics.ust.hk/Jlfdr.html 。
补充数据可在《生物信息学》在线获取。
